This paper addresses control for the synchronization of Chen chaotic systems via sector nonlinear inputs. Feedback control, adaptive control, fast sliding mode and robust control approaches based on single state feedb...This paper addresses control for the synchronization of Chen chaotic systems via sector nonlinear inputs. Feedback control, adaptive control, fast sliding mode and robust control approaches based on single state feedback controller are investigated. In these cases, sufficient conditions for the synchronization are obtained analytically. Numerical simulations verify the control performances.展开更多
Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models a...Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models are hindered by insufficient training samples,because data collection and labeling are very expensive and time-consuming.To solve this issue,a privacy-preserving federated transfer learning approach for disaster classification(FedTL)is proposed,which can allow distributed social computing nodes to collaboratively train a comprehensive model.In the FedTL,Paillier homomorphic encryption method is used to protect the social computing nodes’data privacy.In particular,the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system.The FedTL is verified by a real disaster image dataset collected from social networks.Theoretical analyses and experiment results show that the FedTL is effective,secure,efficient.In addition,the FedTL is highly extensible and can be easily applied in other transfer learning models.展开更多
基金This work was partially supported by Nature Science Foundation of China (No. 60374037, 60574036)the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20050055013)the Program for New Century Excellent Talents of China (NCET)
文摘This paper addresses control for the synchronization of Chen chaotic systems via sector nonlinear inputs. Feedback control, adaptive control, fast sliding mode and robust control approaches based on single state feedback controller are investigated. In these cases, sufficient conditions for the synchronization are obtained analytically. Numerical simulations verify the control performances.
基金The authors gratefully acknowledge the financial support provided by National Science and Technology Major Project of China(No.2018YFB0204304)National Natural Science Foundation of China(No.51909200)Tianjin Research Innovation Project for Postgraduate Stu-dents(No.2019YJSB067).
文摘Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models are hindered by insufficient training samples,because data collection and labeling are very expensive and time-consuming.To solve this issue,a privacy-preserving federated transfer learning approach for disaster classification(FedTL)is proposed,which can allow distributed social computing nodes to collaboratively train a comprehensive model.In the FedTL,Paillier homomorphic encryption method is used to protect the social computing nodes’data privacy.In particular,the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system.The FedTL is verified by a real disaster image dataset collected from social networks.Theoretical analyses and experiment results show that the FedTL is effective,secure,efficient.In addition,the FedTL is highly extensible and can be easily applied in other transfer learning models.
